gensim topic modeling (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/srisuma/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Run in python console\n",
    "import nltk; nltk.download('stopwords')\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "\n",
    "# Gensim\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# spacy for lemmatization\n",
    "import spacy\n",
    "\n",
    "\n",
    "# Plotting tools\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim  # don't skip this\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Enable logging for gensim - optional\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data_full=pd.read_csv('Amazon_Unlocked_Mobile.csv',error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_text = data_full[['Reviews']]\n",
    "data_text=data_text.iloc[:400000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  Reviews\n",
      "0       I feel so LUCKY to have found this used (phone...\n",
      "1       nice phone, nice up grade from my pantach revu...\n",
      "2                                            Very pleased\n",
      "3       It works good but it goes slow sometimes but i...\n",
      "4       Great phone to replace my lost phone. The only...\n",
      "...                                                   ...\n",
      "399995  Very disappointed... I had trouble sending pic...\n",
      "399996  I love all the features that this phone has. I...\n",
      "399997  pro:good lightingdecent battery lifecon:slow p...\n",
      "399998  I could never use the phone: be careful with w...\n",
      "399999                                         good phone\n",
      "\n",
      "[400000 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "pprint(data_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "399938\n"
     ]
    }
   ],
   "source": [
    "data_text.head()\n",
    "data_text=data_text.dropna()\n",
    "print(len(data_text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NLTK Stop words\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use','phone','cellphone','smartphone','be','randomly','mom'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['I feel so LUCKY to have found this used (phone to us & not used hard at '\n",
      " 'all), phone on line from someone who upgraded and sold this one. My Son '\n",
      " 'liked his old one that finally fell apart after 2.5+ years and didnt want an '\n",
      " 'upgrade!! Thank you Seller, we really appreciate it & your honesty re: said '\n",
      " 'used phone.I recommend this seller very highly & would but from them again!!']\n"
     ]
    }
   ],
   "source": [
    "# Convert to list\n",
    "data = data_text.Reviews.values.tolist()\n",
    "\n",
    "# Remove Emails\n",
    "data = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in data]\n",
    "\n",
    "# Remove new line characters\n",
    "data = [re.sub('\\s+', ' ', sent) for sent in data]\n",
    "\n",
    "# Remove distracting single quotes\n",
    "data = [re.sub(\"\\'\", \"\", sent) for sent in data]\n",
    "\n",
    "pprint(data[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_words=['screen','size','touch','battery','charge','charger','hours','power','life','camera','light','pictures','photos','front','rear']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ele in data:\n",
    "    if len(ele.split())<20:\n",
    "        data.remove(ele)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sentence in data:\n",
    "    flag=0\n",
    "    for word in sentence:\n",
    "             if word in key_words:\n",
    "                    flag=1\n",
    "    if flag==0:\n",
    "        data.remove(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "168163\n"
     ]
    }
   ],
   "source": [
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['nice', 'phone', 'nice', 'up', 'grade', 'from', 'my', 'pantach', 'revue', 'very', 'clean', 'set', 'up', 'and', 'easy', 'set', 'up', 'never', 'had', 'an', 'android', 'phone', 'but', 'they', 'are', 'fantastic', 'to', 'say', 'the', 'least', 'perfect', 'size', 'for', 'surfing', 'and', 'social', 'media', 'great', 'phone', 'samsung']]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_words = list(sent_to_words(data))\n",
    "\n",
    "print(data_words[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['nice', 'phone', 'nice', 'up', 'grade', 'from', 'my', 'pantach', 'revue', 'very', 'clean', 'set', 'up', 'and', 'easy', 'set', 'up', 'never', 'had', 'an', 'android', 'phone', 'but', 'they', 'are', 'fantastic', 'to', 'say', 'the', 'least', 'perfect', 'size', 'for', 'surfing', 'and', 'social_media', 'great', 'phone', 'samsung']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# See trigram example\n",
    "print(trigram_mod[bigram_mod[data_words[0]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions for stopwords, bigrams, trigrams and lemmatization\n",
    "def remove_stopwords(texts):\n",
    "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_bigrams(texts):\n",
    "    return [bigram_mod[doc] for doc in texts]\n",
    "\n",
    "def make_trigrams(texts):\n",
    "    return [trigram_mod[bigram_mod[doc]] for doc in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ']):\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['nice', 'nice', 'grade', 'pantach', 'clean', 'easy', 'set', 'fantastic', 'perfect', 'size', 'great', 'samsung']]\n"
     ]
    }
   ],
   "source": [
    "# Remove Stop Words\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "\n",
    "# Form Bigrams\n",
    "data_words_bigrams = make_bigrams(data_words_nostops)\n",
    "\n",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "# python3 -m spacy download en\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ'])\n",
    "\n",
    "print(data_lemmatized[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "168163\n"
     ]
    }
   ],
   "source": [
    "print(len(data_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 2), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1)]]\n"
     ]
    }
   ],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Create Corpus\n",
    "texts = data_lemmatized\n",
    "\n",
    "# Term Document Frequency\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# View\n",
    "print(corpus[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'clean'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('clean', 1),\n",
       "  ('easy', 1),\n",
       "  ('fantastic', 1),\n",
       "  ('grade', 1),\n",
       "  ('great', 1),\n",
       "  ('nice', 2),\n",
       "  ('pantach', 1),\n",
       "  ('perfect', 1),\n",
       "  ('samsung', 1),\n",
       "  ('set', 1),\n",
       "  ('size', 1)]]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Human readable format of corpus (term-frequency)\n",
    "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=10,\n",
    "                                 \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.081*\"love\" + 0.059*\"bad\" + 0.056*\"android\" + 0.043*\"review\" + '\n",
      "  '0.043*\"version\" + 0.043*\"bit\" + 0.039*\"hand\" + 0.038*\"much\" + 0.026*\"sound\" '\n",
      "  '+ 0.025*\"awesome\"'),\n",
      " (1,\n",
      "  '0.053*\"app\" + 0.046*\"thing\" + 0.030*\"video\" + 0.027*\"year\" + 0.025*\"old\" + '\n",
      "  '0.024*\"hot\" + 0.024*\"compact\" + 0.023*\"easy\" + 0.022*\"design\" + '\n",
      "  '0.021*\"light\"'),\n",
      " (2,\n",
      "  '0.083*\"card\" + 0.045*\"sim\" + 0.045*\"long\" + 0.041*\"warranty\" + 0.041*\"able\" '\n",
      "  '+ 0.038*\"money\" + 0.034*\"return\" + 0.032*\"network\" + 0.024*\"store\" + '\n",
      "  '0.020*\"part\"'),\n",
      " (3,\n",
      "  '0.122*\"problem\" + 0.090*\"nice\" + 0.077*\"small\" + 0.065*\"big\" + 0.051*\"week\" '\n",
      "  '+ 0.046*\"many\" + 0.033*\"side\" + 0.033*\"right\" + 0.029*\"memory\" + '\n",
      "  '0.023*\"speed\"'),\n",
      " (4,\n",
      "  '0.043*\"feature\" + 0.043*\"size\" + 0.039*\"call\" + 0.032*\"charge\" + '\n",
      "  '0.032*\"case\" + 0.027*\"charger\" + 0.020*\"pocket\" + 0.018*\"full\" + '\n",
      "  '0.017*\"text\" + 0.016*\"touch\"'),\n",
      " (5,\n",
      "  '0.159*\"good\" + 0.126*\"great\" + 0.062*\"phone\" + 0.043*\"device\" + '\n",
      "  '0.028*\"fast\" + 0.028*\"well\" + 0.027*\"little\" + 0.025*\"price\" + '\n",
      "  '0.022*\"amazing\" + 0.021*\"software\"'),\n",
      " (6,\n",
      "  '0.155*\"screen\" + 0.092*\"time\" + 0.068*\"issue\" + 0.043*\"month\" + '\n",
      "  '0.031*\"button\" + 0.029*\"update\" + 0.027*\"last\" + 0.025*\"display\" + '\n",
      "  '0.024*\"first\" + 0.021*\"happy\"'),\n",
      " (7,\n",
      "  '0.136*\"camera\" + 0.095*\"day\" + 0.068*\"quality\" + 0.045*\"picture\" + '\n",
      "  '0.041*\"lot\" + 0.024*\"speaker\" + 0.024*\"low\" + 0.020*\"high\" + '\n",
      "  '0.020*\"beautiful\" + 0.014*\"internet\"'),\n",
      " (8,\n",
      "  '0.099*\"work\" + 0.094*\"product\" + 0.084*\"new\" + 0.050*\"mobile\" + '\n",
      "  '0.048*\"perfect\" + 0.046*\"seller\" + 0.045*\"service\" + 0.025*\"star\" + '\n",
      "  '0.025*\"customer\" + 0.023*\"sure\"'),\n",
      " (9,\n",
      "  '0.223*\"battery\" + 0.099*\"life\" + 0.094*\"excellent\" + 0.038*\"water\" + '\n",
      "  '0.037*\"people\" + 0.035*\"cell\" + 0.035*\"power\" + 0.032*\"fine\" + '\n",
      "  '0.021*\"brand\" + 0.020*\"point\"')]\n"
     ]
    }
   ],
   "source": [
    "# Print the Keyword in the 10 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -7.341749759191215\n",
      "\n",
      "Coherence Score:  0.6097044198029935\n"
     ]
    }
   ],
   "source": [
    "# Compute Perplexity\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nikhithabalagani/ml/env/lib/python3.6/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
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       "\n",
       "\n",
       "<div id=\"ldavis_el48371398884875022562086268813\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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0.9984802603721619, 0.9933833479881287, 0.9977440237998962, 0.9807014465332031, 0.9962629675865173, 0.9866055846214294, 0.9914883375167847, 0.9925086498260498, 0.9947160482406616, 0.9524742960929871, 0.9910420179367065, 0.9555802345275879, 0.9770807027816772, 0.9987378120422363, 0.9848468899726868, 0.9993593096733093, 0.9993206858634949, 0.9811416268348694, 0.9845675230026245, 0.9863627552986145, 0.9985641241073608, 0.996767520904541, 0.9885458946228027, 0.9942728877067566, 0.988132894039154, 0.9830803275108337, 0.9868977069854736, 0.9827809929847717, 0.9716894030570984, 0.9953290820121765, 0.9797371625900269, 0.9888119101524353, 0.9706296920776367, 0.9882672429084778, 0.9938482642173767, 0.9964895844459534, 0.9946056008338928, 0.9971765875816345, 0.9944311380386353, 0.9936881065368652, 0.9985102415084839, 0.9992835521697998, 0.9931142926216125, 0.9869831800460815, 0.9986515045166016, 0.9462072849273682, 0.997631847858429, 0.9896177053451538, 0.9891818761825562, 0.9899308085441589, 0.9967370629310608, 0.954700767993927, 0.9909898042678833, 0.957570493221283, 0.9917134642601013, 0.977327823638916, 0.9978711605072021, 0.9711425304412842, 0.9888941049575806, 0.9909232258796692, 0.9976740479469299, 0.9735018610954285, 0.9929059147834778, 0.9876937866210938, 0.9926342368125916, 0.9852744936943054, 0.9873122572898865, 0.9874932169914246, 0.9920822978019714, 0.9961941242218018, 0.9738256335258484, 0.9945706129074097, 0.997020423412323, 0.9837735891342163, 0.9939948916435242, 0.9883934855461121, 0.9477038383483887, 0.9923523664474487, 0.974575936794281, 0.9791385531425476, 0.9569134712219238, 0.996200442314148, 0.9979708194732666, 0.9975379109382629, 0.9861337542533875, 0.9992802739143372, 0.993945300579071, 0.9952784776687622, 0.9904522895812988, 0.9779719710350037, 0.9789084792137146, 0.9846553802490234, 0.9951677918434143, 0.9974929690361023, 0.9855160713195801, 0.9770340919494629, 0.963832437992096, 0.9886408448219299, 0.9910998344421387, 0.9755830764770508, 0.9826593399047852, 0.9944338798522949, 0.992896556854248, 0.9807835817337036, 0.9937929511070251, 0.9953334331512451, 0.9868646264076233, 0.9903489947319031, 0.9983910918235779, 0.9982537031173706, 0.9939863681793213, 0.989430844783783, 0.9804148077964783, 0.978682816028595, 0.9855567216873169, 0.9994320273399353, 0.9804627895355225, 0.995540201663971, 0.9958974719047546, 0.9959750771522522, 0.9921830892562866], \"Term\": [\"able\", \"access\", \"accessory\", \"account\", \"activation\", \"amazing\", \"amazon\", \"amount\", \"annoying\", \"app\", \"apple\", \"arrive\", \"att\", \"awesome\", \"bad\", \"battery\", \"big\", \"bill\", \"bit\", \"blue\", \"bottom\", \"box\", \"brand\", \"broke\", \"broken\", \"business\", \"button\", \"buy\", \"buying\", \"cable\", \"call\", \"camera\", \"capacity\", \"car\", \"card\", \"carrier\", \"case\", \"cause\", \"cell\", \"certain\", \"charge\", \"charger\", \"charm\", \"cheap\", \"claim\", \"clarity\", \"clean\", \"color\", \"communication\", \"company\", \"complaint\", \"computer\", \"condition\", \"connect\", \"contact\", \"contract\", \"cord\", \"couple\", \"cover\", \"crap\", \"current\", \"customer\", \"damage\", \"date\", \"datum\", \"daughter\", \"day\", \"deal\", \"defective\", \"delivery\", \"dent\", \"described\", \"description\", \"device\", \"dirty\", \"disappointed\", \"dollar\", \"downside\", \"due\", \"earphone\", \"easy\", \"effective\", \"email\", \"enough\", \"excelent\", \"excellent\", \"expectation\", \"expense\", \"expensive\", \"experience\", \"extra\", \"fact\", \"fast\", \"fine\", \"first\", \"fit\", \"fix\", \"flawless\", \"friend\", \"frustrating\", \"full\", \"function\", \"get\", \"gift\", \"gig\", \"glass\", \"good\", \"granddaughter\", \"grandson\", \"great\", \"guy\", \"half\", \"happy\", \"hard\", \"hassle\", \"headphone\", \"heavy\", \"help\", \"horrible\", \"hour\", \"hurry\", \"ill\", \"impressed\", \"info\", \"insurance\", \"iphone\", \"issue\", \"item\", \"junk\", \"key\", \"kid\", \"kit\", \"last\", \"less\", \"letter\", \"life\", \"line\", \"listing\", \"little\", \"local\", \"lock\", \"long\", \"look\", \"lot\", \"love\", \"low\", \"major\", \"many\", \"memory\", \"mention\", \"metro_pc\", \"microphone\", \"minor\", \"minute\", \"mistake\", \"mobile\", \"model\", \"money\", \"month\", \"much\", \"nervous\", \"network\", \"new\", \"next\", \"nice\", \"notice\", \"noticeable\", \"number\", \"okay\", \"old\", \"one\", \"online\", \"open\", \"order\", \"original\", \"overall\", \"package\", \"part\", \"pay\", \"people\", \"perfect\", \"person\", \"phone\", \"photo\", \"picture\", \"pink\", \"plan\", \"plastic\", \"pleased\", \"plugged\", \"power\", \"present\", \"previous\", \"price\", \"primary\", \"problem\", \"product\", \"program\", \"protector\", \"provider\", \"purchase\", \"quality\", \"question\", \"quick\", \"read\", \"real\", \"reason\", \"reasonable\", \"recommend\", \"refund\", \"refurbished\", \"regular\", \"reliable\", \"repair\", \"replace\", \"replacement\", \"return\", \"review\", \"right\", \"satisfied\", \"scratch\", \"screen\", \"search\", \"self\", \"seller\", \"selling\", \"service\", \"set\", \"several\", \"shape\", \"shipping\", \"shoddy\", \"short\", \"shut\", \"side\", \"significant\", \"sim\", \"single_scratch\", \"sister\", \"skeptical\", \"small\", \"solution\", \"son\", \"song\", \"sound\", \"space\", \"speaker\", \"speed\", \"sprint\", \"star\", \"static\", \"storage\", \"store\", \"support\", \"sure\", \"surprise\", \"sweet\", \"switch\", \"system\", \"task\", \"tear\", \"text\", \"thank\", \"thing\", \"thought\", \"time\", \"today\", \"top\", \"trouble\", \"turn\", \"unable\", \"unlimited\", \"unlock\", \"unlocked\", \"unsatisfied\", \"unusable\", \"unused\", \"update\", \"upgrade\", \"upset\", \"useful\", \"useless\", \"value\", \"vendor\", \"verizon\", \"warranty\", \"waste\", \"water\", \"way\", \"week\", \"well\", \"white\", \"wireless\", \"wish\", \"wonderful\", \"work\", \"working\", \"worth\", \"wrong\", \"year\", \"yesterday\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [10, 9, 1, 8, 3, 2, 4, 5, 7, 6]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el48371398884875022562086268813\", ldavis_el48371398884875022562086268813_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el48371398884875022562086268813\", ldavis_el48371398884875022562086268813_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el48371398884875022562086268813\", ldavis_el48371398884875022562086268813_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "9     -0.433357 -0.013511       1        1  18.647036\n",
       "8     -0.000279 -0.198921       2        1  11.755056\n",
       "0      0.063220 -0.033829       3        1  11.711370\n",
       "7      0.013998  0.405602       4        1   9.965907\n",
       "2      0.059242 -0.033796       5        1   9.107787\n",
       "1      0.063615 -0.026743       6        1   8.900707\n",
       "3      0.064210 -0.022114       7        1   8.494212\n",
       "4      0.061897 -0.024261       8        1   8.023774\n",
       "6      0.055134 -0.028967       9        1   7.468640\n",
       "5      0.052318 -0.023460      10        1   5.925513, topic_info=     Category         Freq     Term        Total  loglift  logprob\n",
       "4     Default  3275.000000    great  3275.000000  30.0000  30.0000\n",
       "23    Default  2475.000000      new  2475.000000  29.0000  29.0000\n",
       "60    Default  2508.000000     good  2508.000000  28.0000  28.0000\n",
       "127   Default  1555.000000  product  1555.000000  27.0000  27.0000\n",
       "54    Default  1531.000000     time  1531.000000  26.0000  26.0000\n",
       "...       ...          ...      ...          ...      ...      ...\n",
       "642   Topic10    22.055559  selling    23.250719   2.7731  -5.6115\n",
       "413   Topic10    21.784960     lock    22.979950   2.7725  -5.6238\n",
       "2756  Topic10    21.811657   shoddy    23.043869   2.7709  -5.6226\n",
       "436   Topic10    20.345051     okay    21.540361   2.7688  -5.6922\n",
       "2855  Topic10    20.572281   unused    21.788019   2.7685  -5.6811\n",
       "\n",
       "[331 rows x 6 columns], token_table=      Topic      Freq        Term\n",
       "term                             \n",
       "68        3  0.996238        able\n",
       "297       8  0.986906      access\n",
       "761       9  0.995295   accessory\n",
       "756       7  0.992401     account\n",
       "2675      7  0.967378  activation\n",
       "...     ...       ...         ...\n",
       "1604      9  0.980463     working\n",
       "86        4  0.995540       worth\n",
       "107       5  0.995897       wrong\n",
       "188       8  0.995975        year\n",
       "1837      5  0.992183   yesterday\n",
       "\n",
       "[301 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[10, 9, 1, 8, 3, 2, 4, 5, 7, 6])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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